Biology image recognition, the computer extraction of regions containing biological objects such as tissue, cellular and subcellular components, bacteria, viruses of interest in microscopy images, is a fundamental step in quantitative microscopy which has broad applications and markets in basic research, drug discovery, and disease diagnosis. Biology image recognition consists of two major steps (1) a biological region segmentation step (Lee J S J. “Learnable object segmentation”, U.S. Pat. No. 7,203,360, Apr. 10, 2007) followed by (2) a region partitioning step. Biology region segmentation identifies the regions in the image where biological objects of interest occupy. Region partitioning identifies the individual objects among the segmented regions for individual object counting and measurements.
Biological objects such as live cells often exist in aggregates (colonies) rather than in isolation. Therefore it is important to separate them from the acquired images of the biological objects for characterization and measurements. Region partitioning step separates individual biology regions to enable individual biology characterization. It enables biological object counting, for comprehensive individual biological object morphological characterization and for biological object type classification, population statistics and for image cytometry. These have broad applications in basic research, cancer research, toxicology and drug discovery.
Currently, most users perform biology image recognition using standard image processing software (such as Zeiss' AxioVision, Nikon's NIS-Elements, Olympus cellSens, ImageJ, Metamorph, ImagePro, Slidebook, Imaris, Volocity etc.), custom scripts/programming, or by hand. It is difficult to apply standard image processing software functions to perform biology image recognition. As a result the majority of biology recognition is performed either manually or using a simple intensity threshold that has very limited applications. Some software supports plug-ins. Yet plug-ins developed in one lab for image recognition rarely work for the application of a second lab. The users have to modify the algorithm parameters, or even the code itself.
Biology image recognition products have been developed recently for high content screening applications. However, they are coupled to specific instrument platforms, cell types, and reagents. They are not flexible for broad applications. The current immature microscopy biology recognition tools impose cost barriers on scientists and the image based scientific discovery process. The cost in skilled labor for manual recognition and custom script development is high. A greater cost is that of experiments foregone, or data uncollected, due to problems related to image recognition.
Prior art region partition methods relying on a simple but unrealistic assumption that is the background has the lowest intensity and the objects have smooth and stable intensity distribution with the lowest values at boundary and highest values around the center of the objects. Unfortunately, this assumption does not match the reality of biological objects. Special dyes are often used to stain biological objects to match the intensity distribution assumptions. However, special staining causes toxic effect that cannot be widely used in live object experiments. Also, object aggregation creates problem of separation due to obscuration and boundary overlapping.
Special prior art algorithms were programmed to handle specialized situation yet they are not general purpose and cannot be flexibly adopted to other applications (Niels VAN VLIET, “Image Segmentation Applied to Cytology”, Technical Report no 0303, Laboratoire de Recherche et Développement de l'Epita, —June 2003). There is a strong need for a general purpose new method that (1) can handle broad range of applications, not just custom algorithm for specialized applications; (2) objects that do not have smooth and stable intensity distribution; (3) objects that overlap,